Liang Zhi-Pei, Madore Bruno, Glover Gary H, Pelc Norbert J
IEEE Trans Med Imaging. 2003 Aug;22(8):1026-30. doi: 10.1109/TMI.2003.815896.
Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.
许多成像实验都涉及获取图像的时间序列。为了提高成像速度，已经提出了几种“数据共享”方法，这些方法收集一个（或几个）高分辨率参考数据以及一系列降采样数据集。在图像重建中，已经使用了两种方法，即“锁孔”方法和通过广义序列重建（RIGR）的降编码成像。“锁孔”方法只是简单地用参考数据集中的高频数据来填充未测量的高频数据，而RIGR则使用广义序列（GS）模型来恢复未测量的数据，该模型的基函数是基于参考图像构建的。本文介绍了一种基于GS的图像重建快速算法（以及两种扩展算法）。所提出的算法与“锁孔”算法具有相同的计算复杂度，但更能够捕捉高分辨率动态信号变化。
